code: def forward(self, inputs, targets=None):
      others = targets[-4:]
      encoder_word_pos = others[0]
      gsrm_word_pos = others[1]
      gsrm_slf_attn_bias1 = others[2]
      gsrm_slf_attn_bias2 = others[3]
  
      pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos)
  
      gsrm_feature, word_out, gsrm_out = self.gsrm(
          pvam_feature,
          gsrm_word_pos,
          gsrm_slf_attn_bias1,
          gsrm_slf_attn_bias2,
      )
  
      final_out = self.vsfd(pvam_feature, gsrm_feature)
      if not self.training:
          final_out = F.softmax(final_out, axis=1)
  
      _, decoded_out = paddle.topk(final_out, k=1)
  
      predicts = OrderedDict(
          [
              ("predict", final_out),
              ("pvam_feature", pvam_feature),
              ("decoded_out", decoded_out),
              ("word_out", word_out),
              ("gsrm_out", gsrm_out),
          ]
      )
  
      return predicts
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn, ParamAttr
  from paddle.nn import functional
  import numpy
  from .self_attention import WrapEncoderForFeature
  from .self_attention import WrapEncoder
  from paddle.static import Program
  from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the SRNHead obj's underlying member method code with example usage: obj.forward(inputs, targets) and returns: predicts